Introduction: Slope Entropy (SlopEn) is a robust estimator of the degree of complexity in physiological time series. Patients affected by post-ischemic ventricular tachycardia (VT) present highly fragmented and unstructured intracardiac electrograms (EGMs), hereafter called abnormal ventricular potentials (AVPs), referable to areas that trigger and/or maintain the arrhythmia. Thus, quantifying EGMs complexity through entropy measures can support AVPs identification during the electrophysiological study. Hence, this study aims to characterize VT EGMs, specifically physiological EGMs and AVPs, in terms of SlopEn metric. Methods: A dataset of EGMs composed of 119 physiological signals and 225 AVPs, from four patients, was used. Segments of 500 ms were selected from each EGM, and normalized according to the min-max method and centered. On this basis, the impact of the embedded dimension m, the vertical increment threshold γ and the proximity-to-zero difference threshold δ has been assessed, exploring: m from 3 up to 10, γ from 1° to 45°, δ from 0.1° up to 0.9°. Non-parametric statistical tests were used along with Bonferroni correction to assess the presence of significant differences between the two classes for each parameter combination. Results: The distributions of the two EGM classes significantly differed for the tested m values, and best findings were achieved by m=3 (p < 0.0001). Keeping fixed this value, the best set of parameters is obtained with γ in the range 20°÷45° and δ in the range 0.1°÷0.5° (p < 0.0001). Conclusions: SlopEn allows for the effective characterization of the EGMs, and paves the way for the use of such complexity metric to support for the identification of arrhythmogenic areas in VT electrophysiological studies. Hence, future developments of this work could lead to the development of novel three-dimensional maps leveraging EGMs complexity to support the substrate analysis.